# -*- coding: utf-8 -*-
from datetime import timedelta

from jms.ods.oms.yl_oms_order_third_ext import jms_ods__yl_oms_order_third_ext
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator

jms_dwd__dwd_yl_oms_order_third_ext_base_dt = SparkSqlOperator(
    task_id='jms_dwd__dwd_yl_oms_order_third_ext_base_dt',
    task_concurrency=1,
    pool_slots=6,
    master='yarn',
    name='jms_dwd__dwd_yl_oms_order_third_ext_base_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dwd/oms/dwd_yl_oms_order_third_ext_base_dt/execute.sql',
    driver_memory='6G',
    driver_cores=2,
    executor_cores=10,
    executor_memory='8G',
    email=['rabie.zhuang@jtexpress.com','yl_bigdata@yl-scm.com'],
    num_executors=10,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 20,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '5G',  # 堆外内存
          'spark.sql.shuffle.partitions': 1000,
          },
    # hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
    #           'hive.exec.dynamic.partition.mode': 'nonstrict',
    #           'hive.exec.max.dynamic.partitions': 91,  # 每天生成 60 个分区
    #           'hive.exec.max.dynamic.partitions.pernode': 91,  # 每天生成 60 个分区
    #           },
    yarn_queue='pro',
    execution_timeout=timedelta(minutes=60),
)

jms_dwd__dwd_yl_oms_order_third_ext_base_dt << [jms_ods__yl_oms_order_third_ext]
